Question

What are some other factors that companies should consider when using data analytics?

Answer 1

Today, enterprises are leveraging big data analytic tools to derive valuable business insights from the gathered information. Big data analytic tools not only help enterprises in examining vast data sets and identifying patterns but also assists in developing predictive and prescriptive models. Implementing these tools with the current business can enhance operational efficiency, productivity, revenue opportunities and other enterprise benefits.

Avoid point solution

Purchasing a big data tool to address only one of the concerns may not be a good idea. The most common mistake enterprises do while buying big data tools is not considering the requirement of the entire organization. Buying point solution based on one aspect will mitigate the organization’s ability to convert big data into valuable insights. Hence, it is necessary to keep all stakeholders in a loop, in order to share the information seamlessly and help the business grow successfully.

Democratize the data

Big data solutions are built for specific kind of users with explicit skills, such as analysts, software team or data administrators. These are the ones who will execute the queries and provide insights for the whole organization. In order to get the better part of big data investment with evolving technology and plethora of business needs, the collected data must be democratized and made available across the entire organization. Mainly, to the stakeholders who make crucial and instantaneous decisions that will directly influence revenue.

Reusability factor

Next point to remember would be implementation, a common blunder made by companies while solving the latest problem is using a new tool with an old-school method. That is designing software to address single use case. It is highly important that software solutions bought must have an excessive reusability factor. The analytic tools should be able to learn the pattern and insights of data when it addresses the problem, so executives don’t have to trace the source data every time to solve enterprise issues. For instance, if the software is designed to run in a specific region, it fails to run in other regions with different parameters, the developer has to rebuild software from scratch to address this issue. With the reusability factor, the software has a separate layer independent from use cases that extracts valuable signals and implement it to solve a business problem. This enables users to develop more use cases to address many other business issues.

Data management

The biggest challenge would be converting big data and analytic insights into valid results. The enterprise must extract insights to achieve complete value from analytics initiatives of any scale or scope. Unstructured data contains a lot of unwanted segment that needs to be filtered out to make sensible data. It’s a good practice to find software that converts big data into small data and parse the information into more actionable insights that solve the issues.

Adaptability

The evolving technology and change in environment have influenced organizations to choose the software solution that is adaptable to all the conditions. If the enterprise has more than one component in the architecture, it’s better to deploy a platform with a built-in technology stack that integrates best components. This will simplify the infrastructure, cuts down the number of IT integration and ensure progress of the environment with transforming technology.

Select a big data analytics tool that offers following benefits:

Big data tools offer several benefits, and all the business advantage revolves around these factors:

Error notification: The tool should help organizations to identify the flaws and take a quick action against errors. It also should assist in optimizing operational efficiency.

New business strategies: A big data analytic tool should give enterprise an edge over competitors to be ahead of them by notifying the latest business strategies. According to the QuinStreet Big Data Research Study, over half of the enterprises that leveraged big data analytic tools were able to enhance consumer retention, develop efficient products and gain a competitive benefit.

Better service: Analytic tools should help in improving the service and initiates extra revenue and higher conversion rate. It should offer flexibility to the enterprise to monitor the products used by its clients and take immediate action prior to failure. For instance, if the system is about the crash, the organization monitoring will be notified and allows them to take necessary action before the failure.

Cost-effective: Big data analytic tools should allow organizations to store large amounts of data at a reasonable rate than a conventional database. Although implementation of the analytic tools might be steep, it saves enough money for the enterprise. For instance, organizations looking to save the cost would prefer open source Hadoop cluster. Companies store long-term data in Hadoop instead of augmenting the data warehouse. Data is later shifted to the conventional database for examination and production as needed.      

Sales growth: Big data tool should give a holistic picture of how the sales are performing; if the sales are declining then it can take quick action to avoid losing revenue and increasing the sales.

Keep customer Up-to-date: Information on latest offers and promotions keeps customer up-to-date about the latest customer trends.   

These are the few benefits that an enterprise experience when they select appropriate data analytic tools, increase in the revenue, reduction of cost, optimization of operational efficiency, enhanced customer care and security.

How big companies deal with big data?

Big companies leverage big data tools to process large and varied data sets across clustered systems.

IBM

IBM has set up its big data offering on Hadoop, like all the other big companies. The affordable open source platform enables the enterprise to capture, store, manage and examine huge volumes of data from numerous sources with its BigInsights product. This product helps organizations in making rapid decisions. IBM BigInsights delivers Hadoop as a Service through IBM SoftLayer cloud infrastructure. IBM streams allow the organization to capture and analyze data during the action and deliver quick insights on crucial IoT applications.

HP

HP, another popular name in IT provides big data solutions. HP offers a self built platform that allows organizations to make a rapid decision for better results. HP also assists in developing IT architecture that enables the enterprise to manage the volume, variety, velocity, veracity, and value of data. HP uses Hadoop to capture and invent crucial data, and then they enhance analytical performance on HPE Vertica. HPE IDOL software offers a particular platform for structured, semi-structured and unstructured data. With the help of natural language processing and statistical technique, HP is able to utilize hybrid analytics for identifying patterns, concepts, trends, and relationships.

Amazon

Amazon is a renowned name in the industry for offering web hosting and other services. The advantage of using these unmatched services is to reduce the cost and uptime. Amazon focuses on providing a fundamental framework for its clients in order to cut down the cost, and concentrate less on customer support. Amazon embraces data analytic tools like Hive, Hadoop, Spark, and Pig that lets them develop their own big data stack by building the solution on their own platform.

Conclusion

Big data is no more an emerging technology, it has been leveraged by many companies to steer their organization towards success. Although enterprises are tempted to follow the leading vendor, it is better to choose an innovative company who offers innovative services than following the herd. It takes a lot of efforts and time to deploy big data solution, but once the right provider is chosen and the foundation is designed firmly then the organization can experience better benefits from big data.